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Programming chemical kinetics: engineering dynamic reaction networks with DNA strand displacement

Citation

Srinivas, Niranjan (2015) Programming chemical kinetics: engineering dynamic reaction networks with DNA strand displacement. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/Z9KD1VVJ. http://resolver.caltech.edu/CaltechTHESIS:06012015-171243593

Abstract

Over the last century, the silicon revolution has enabled us to build faster, smaller and more sophisticated computers. Today, these computers control phones, cars, satellites, assembly lines, and other electromechanical devices. Just as electrical wiring controls electromechanical devices, living organisms employ "chemical wiring" to make decisions about their environment and control physical processes. Currently, the big difference between these two substrates is that while we have the abstractions, design principles, verification and fabrication techniques in place for programming with silicon, we have no comparable understanding or expertise for programming chemistry.

In this thesis we take a small step towards the goal of learning how to systematically engineer prescribed non-equilibrium dynamical behaviors in chemical systems. We use the formalism of chemical reaction networks (CRNs), combined with mass-action kinetics, as our programming language for specifying dynamical behaviors. Leveraging the tools of nucleic acid nanotechnology (introduced in Chapter 1), we employ synthetic DNA molecules as our molecular architecture and toehold-mediated DNA strand displacement as our reaction primitive.

Abstraction, modular design and systematic fabrication can work only with well-understood and quantitatively characterized tools. Therefore, we embark on a detailed study of the "device physics" of DNA strand displacement (Chapter 2). We present a unified view of strand displacement biophysics and kinetics by studying the process at multiple levels of detail, using an intuitive model of a random walk on a 1-dimensional energy landscape, a secondary structure kinetics model with single base-pair steps, and a coarse-grained molecular model that incorporates three-dimensional geometric and steric effects. Further, we experimentally investigate the thermodynamics of three-way branch migration. Our findings are consistent with previously measured or inferred rates for hybridization, fraying, and branch migration, and provide a biophysical explanation of strand displacement kinetics. Our work paves the way for accurate modeling of strand displacement cascades, which would facilitate the simulation and construction of more complex molecular systems.

In Chapters 3 and 4, we identify and overcome the crucial experimental challenges involved in using our general DNA-based technology for engineering dynamical behaviors in the test tube. In this process, we identify important design rules that inform our choice of molecular motifs and our algorithms for designing and verifying DNA sequences for our molecular implementation. We also develop flexible molecular strategies for "tuning" our reaction rates and stoichiometries in order to compensate for unavoidable non-idealities in the molecular implementation, such as imperfectly synthesized molecules and spurious "leak" pathways that compete with desired pathways.

We successfully implement three distinct autocatalytic reactions, which we then combine into a de novo chemical oscillator. Unlike biological networks, which use sophisticated evolved molecules (like proteins) to realize such behavior, our test tube realization is the first to demonstrate that Watson-Crick base pairing interactions alone suffice for oscillatory dynamics. Since our design pipeline is general and applicable to any CRN, our experimental demonstration of a de novo chemical oscillator could enable the systematic construction of CRNs with other dynamic behaviors.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:DNA nanotechnology; Chemical Reaction Networks; DNA strand displacement; Molecular Programming; Chemical Kinetics;
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Computation and Neural Systems
Awards:Demetriades-Tsafka-Kokkalis Prize In Nanotechnology Or Related Fields, 2015
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Winfree, Erik
Thesis Committee:
  • Murray, Richard M. (chair)
  • Winfree, Erik
  • Pierce, Niles A.
  • Rothemund, Paul W. K.
  • Elowitz, Michael B.
  • Seelig, Georg
Defense Date:20 May 2015
Record Number:CaltechTHESIS:06012015-171243593
Persistent URL:http://resolver.caltech.edu/CaltechTHESIS:06012015-171243593
DOI:10.7907/Z9KD1VVJ
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:8959
Collection:CaltechTHESIS
Deposited By: Niranjan Srinivas
Deposited On:05 Jun 2015 18:08
Last Modified:12 Apr 2016 21:08

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